用卫星图像检测南亚砖窑，提升污染与强迫劳动监控能力
Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data
- 构建区域自适应图模型ClimateGraph，捕捉砖窑空间布局特征
- 在130万张0.149米分辨率图像上验证，多方法检测性能互补
- 适合关注环境监测、人权保护与遥感应用的研究者
砖窑是南亚地区空气污染和强迫劳动的主要来源，但大规模监测受限于稀疏且过时的地面数据。本文利用高分辨率卫星影像，构建了覆盖南亚与中亚五个区域、包含超过130万张图像块的多城市zoom-20（0.149米/像素）数据集。提出ClimateGraph，一种区域自适应的图神经网络模型，用于捕捉砖窑布局中的空间与方向结构，并与现有图学习基线进行对比评估。同时，评估基于遥感的检测流程，并与近期的遥感基础模型进行基准对比。结果表明，图模型、基础模型与遥感方法各具优势，可互补使用，为卫星图像下规模化砖窑监测提供实用指导。
原文摘要 · Abstract (English)
Brick kilns are a major source of air pollution and forced labor in South Asia, yet large-scale monitoring remains limited by sparse and outdated ground data. We study brick kiln detection at scale using high-resolution satellite imagery and curate a multi city zoom-20 (0.149 meters per pixel) resolution dataset comprising over 1.3 million image tiles across five regions in South and Central Asia. We propose ClimateGraph, a region-adaptive graph-based model that captures spatial and directional structure in kiln layouts, and evaluate it against established graph learning baselines. In parallel, we assess a remote sensing based detection pipeline and benchmark it against recent foundation models for satellite imagery. Our results highlight complementary strengths across graph, foundation, and remote sensing approaches, providing practical guidance for scalable brick kiln monitoring from satellite imagery.
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